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A library for locally weighted projection regression
In this paper we introduce an improved implementation of locally weighted projection regression (LWPR), a supervised learning algorithm that is capable of handling high-dimensional input data. As the key features, our code supports multi-threading, is available for multiple platforms, and provides wrappers for several programming languages.
@article{Klanke_JMLR_2008, title = {A library for locally weighted projection regression}, booktitle = {Journal of Machine Learning Research}, abstract = {In this paper we introduce an improved implementation of locally weighted projection regression (LWPR), a supervised learning algorithm that is capable of handling high-dimensional input data. As the key features, our code supports multi-threading, is available for multiple platforms, and provides wrappers for several programming languages.}, volume = {9}, pages = {623-626}, year = {2008}, note = {clmc}, slug = {klanke_jmlr_2008}, author = {Klanke, S. and Vijayakumar, S. and Schaal, S.}, crossref = {p10244}, url = {http://www-clmc.usc.edu/publications/k/klanke-JMLR2008.pdf} }